Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes

JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY

卷:140

Article

Choi, Sanghun^1,2,3  Hoffman, Eric A.^3,4,5  Wenzel, Sally E.⁶  Castro, Mario^7,8  Fain, Sean⁹  Jarjour, Nizar⁹  Schiebler, Mark L.⁹  Chen, Kun¹⁰  Lin, Ching-Long^1,2,4 

[1] Univ Iowa, Dept Mech & Ind Engn, Iowa City, IA USA

[2] Univ Iowa, IIHR Hydrosci & Engn, Iowa City, IA USA

[3] Univ Iowa, Dept Biomed Engn, Iowa City, IA 52242 USA

[4] Univ Iowa, Dept Radiol, Iowa City, IA 52242 USA

[5] Univ Iowa, Dept Internal Med, Iowa City, IA 52242 USA

[6] Univ Pittsburgh, Div Pulm Allergy & Crit Care Med, Pittsburgh, PA USA

[7] Washington Univ, Sch Med, Dept Internal Med, St Louis, MO 63110 USA

[8] Washington Univ, Sch Med, Dept Pediat, St Louis, MO 63110 USA

[9] Univ Wisconsin, Sch Med & Publ Hlth, Madison, WI 53706 USA

[10] Univ Connecticut, Dept Stat, Storrs, CT 06269 USA

[11] Severe Asthma Res Program, Bethesda, MD USA

关键词: Computed tomography;    image processing;    severe asthma;    air trapping;    image registration;    luminal narrowing;    wall thickening;    airway circularity;    cluster analysis;    neutrophilic asthma;   

DOI  :  10.1016/j.jaci.2016.11.053

来源: Elsevier

【 摘 要 】

Background: Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects. Objective: The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics. Methods: We performed an imaging-based cluster analysis using quantitative computed tomography-based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatic patients. The imaging-based metrics included a broader set of multiscale variables, such as inspiratory airway dimension, expiratory air trapping, and registration-based lung deformation (inspiration vs expiration). Asthma subgroups derived from a clustering method were associated with subject demographics, questionnaire results, medication history, and biomarker variables. Results: Cluster 1 was composed of younger patients with early-onset nonsevere asthma and reversible airflow obstruction and normal airway structure. Cluster 2 was composed of patients with a mix of patients with nonsevere and severe asthma with marginal inflammation who exhibited airway luminal narrowing without wall thickening. Clusters 3 and 4 were dominated by patients with severe asthma. Cluster 3 patients were obese female patients with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older male subjects with persistent airflow obstruction who exhibited significant air trapping and reduced regional deformation. Cluster 3 and 4 patients also showed decreased lymphocyte and increased neutrophil counts, respectively. Conclusions: Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups that can be used as a basis for the development of new therapies.

【 授权许可】

Free